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test_that("makeModelMultiplexerParamSet works", {
bls = list(
makeLearner("classif.ksvm"),
makeLearner("classif.randomForest")
)
lrn = makeModelMultiplexer(bls)
ps1 = makeModelMultiplexerParamSet(
lrn,
makeNumericParam("sigma", lower = -10, upper = 10, trafo = function(x) 2^x),
makeIntegerParam("ntree", lower = 1L, upper = 50L)
)
ps2 = makeModelMultiplexerParamSet(lrn,
classif.ksvm = makeParamSet(makeNumericParam("sigma",
lower = -10,
upper = 10, trafo = function(x) 2^x)),
classif.randomForest = makeParamSet(makeIntegerParam("ntree",
lower = 1L,
upper = 50L))
)
ps3 = makeParamSet(
makeDiscreteParam("selected.learner", values = extractSubList(bls, "id")),
makeNumericParam("classif.ksvm.sigma",
lower = -10, upper = 10,
trafo = function(x) 2^x,
requires = quote(selected.learner == "classif.ksvm")),
makeIntegerParam("classif.randomForest.ntree",
lower = 1L, upper = 50L,
requires = quote(selected.learner == "classif.randomForest"))
)
expect_equal(ps1, ps2)
expect_equal(ps2, ps3)
expect_equal(ps1, ps3)
})
# this is more or less a test for BaseEnsemble, that hyperpars work and so on
test_that("ModelMultiplexer basic stuff works", {
lrn = makeModelMultiplexer(c("classif.lda", "classif.rpart"))
expect_equal(class(lrn), c("ModelMultiplexer", "BaseEnsemble", "Learner"))
# check hyper par setting and so on
lrn2 = setHyperPars(lrn,
selected.learner = "classif.rpart",
classif.rpart.minsplit = 10000L)
xs = getHyperPars(lrn2)
expect_true(setequal(names(xs), c(
"selected.learner",
"classif.rpart.minsplit", "classif.rpart.xval")))
expect_equal(xs$classif.rpart.minsplit, 10000L)
mod = train(lrn2, task = binaryclass.task)
expect_equal(
getLearnerModel(mod, more.unwrap = TRUE)$control$minsplit,
10000L)
# check removal
lrn3 = removeHyperPars(lrn2, "classif.rpart.minsplit")
xs = getHyperPars(lrn3)
expect_true(setequal(names(xs), c("selected.learner", "classif.rpart.xval")))
# check predict.type
lrn2 = setPredictType(lrn, "prob")
mod = train(lrn2, task = binaryclass.task)
p = predict(mod, task = binaryclass.task)
expect_numeric(getPredictionProbabilities(p),
any.missing = FALSE, lower = 0,
upper = 1)
})
test_that("FailureModel works", {
lrn = list(
makeLearner("classif.__mlrmocklearners__2",
config = list(on.learner.error = "warn")),
makeLearner("classif.rpart", config = list(on.learner.error = "warn"))
)
lrn = makeModelMultiplexer(lrn)
lrn = setHyperPars(lrn, classif.__mlrmocklearners__2.alpha = 1)
mod = train(lrn, task = iris.task)
expect_false(isFailureModel(mod))
lrn = setHyperPars(lrn, classif.__mlrmocklearners__2.alpha = 0)
expect_warning({
mod = train(lrn, task = iris.task)
}, "foo")
expect_true(isFailureModel(mod))
tmp = getMlrOptions()$on.learner.error
configureMlr(on.learner.error = "warn")
lrn = setHyperPars(lrn, classif.__mlrmocklearners__2.alpha = 1)
lrn = removeHyperPars(lrn, "selected.learner")
expect_warning({
mod = train(lrn, task = iris.task)
})
expect_true(isFailureModel(mod))
configureMlr(on.learner.error = tmp)
})
test_that("ModelMultiplexer tuning", {
lrn = makeModelMultiplexer(c("classif.knn", "classif.rpart"))
rdesc = makeResampleDesc("CV", iters = 2L)
tune.ps = makeModelMultiplexerParamSet(
lrn,
makeIntegerParam("minsplit", lower = 1, upper = 50))
# tune with random
ctrl = makeTuneControlRandom(maxit = 4L)
res = tuneParams(lrn, binaryclass.task, rdesc,
par.set = tune.ps,
control = ctrl)
expect_true(setequal(class(res), c("TuneResult", "OptResult")))
y = getOptPathY(res$opt.path)
expect_true(all(!is.na(y)))
expect_true(all(is.finite(y)))
# tune with irace
task = subsetTask(binaryclass.task, subset = c(1:20, 150:170))
ctrl = makeTuneControlIrace(
maxExperiments = 40L, nbIterations = 2L,
minNbSurvival = 1L)
res = tuneParams(lrn, task, rdesc, par.set = tune.ps, control = ctrl)
expect_true(setequal(class(res), c("TuneResult", "OptResult")))
y = getOptPathY(res$opt.path)
expect_true(all(!is.na(y)))
expect_true(all(is.finite(y)))
})
# we had bug here, see issue #609
test_that("ModelMultiplexer inherits predict.type from base learners", {
base.learners = list(
makeLearner("classif.ksvm", predict.type = "prob"),
makeLearner("classif.randomForest", predict.type = "prob")
)
learner = makeModelMultiplexer(base.learners)
expect_equal(learner$predict.type, "prob")
# now lets see that the next code runs and does not complain about matrix
# output for base learner predict output
r = holdout(learner, binaryclass.task)
# now check that we can tune the threshold
ps = makeModelMultiplexerParamSet(
learner,
makeDiscreteParam("C", 1),
makeDiscreteParam("mtry", c(2, 3))
)
rdesc = makeResampleDesc("Holdout")
ctrl = makeTuneControlGrid(tune.threshold = TRUE)
res = tuneParams(learner, binaryclass.task,
resampling = rdesc, par.set = ps,
control = ctrl)
})
# we had bug here, see issue #647
test_that("ModelMultiplexer passes on hyper pars in predict", {
requirePackagesOrSkip("glmnet")
base.learners = list(
makeLearner("regr.glmnet"),
makeLearner("regr.rpart")
)
learner = makeModelMultiplexer(base.learners)
expect_equal(learner$predict.type, "response")
r = holdout(learner, regr.task)
})
# issue #707
test_that("ModelMultiplexer handles tasks with no features", {
requirePackagesOrSkip("glmnet")
base.learners = list(
makeLearner("regr.glmnet"),
makeLearner("regr.rpart")
)
learner = makeModelMultiplexer(base.learners)
task = subsetTask(bh.task, features = character(0))
m = train(learner, task)
expect_s3_class(m$learner.model, "NoFeaturesModel")
p = predict(m, task)
expect_s3_class(p$data, "data.frame")
expect_true(all(p$data$response == mean(p$data$response)))
})
# issue #760
test_that("ModelMultiplexer passes on hyper pars in predict with both", {
test.ps = makeRLearnerClassif("test.ps", character(0),
makeParamSet(
makeIntegerLearnerParam("tpTRAIN", when = "train"),
makeIntegerLearnerParam("tpPREDICT", when = "predict"),
makeIntegerLearnerParam("tpBOTH", when = "both")),
properties = c("numerics", "twoclass"))
test.ps$fix.factors.prediction = TRUE
opts = NULL
trainLearner.test.ps = function(.learner, .task, .subset, .weights = NULL,
...) {
opts <<- list(...) # nolint
# the following to make the type checking happy
list(dummy = getTaskData(.task, .subset)[[getTaskTargetNames(.task)[1]]][1])
}
registerS3method("trainLearner", "test.ps", trainLearner.test.ps)
predictLearner.test.ps = function(.learner, .model, .newdata, ...) {
opts <<- list(...) # nolint
rep(.model$learner.model$dummy, nrow(.newdata)) # just do something
}
registerS3method("predictLearner", "test.ps", predictLearner.test.ps)
test.ps.mm = makeModelMultiplexer(list(test.ps))
test.ps.mm.args = setHyperPars(test.ps.mm,
test.ps.tpTRAIN = 1,
test.ps.tpPREDICT = 2, test.ps.tpBOTH = 3)
trained = train(test.ps.mm.args, pid.task)
expect_false(is.null(opts$tpBOTH))
expect_false(is.null(opts$tpTRAIN))
expect_true(is.null(opts$tpPREDICT))
predicted = predict(trained, pid.task)
expect_false(is.null(opts$tpBOTH))
expect_true(is.null(opts$tpTRAIN))
expect_false(is.null(opts$tpPREDICT))
})
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